Methods › General › Self-Supervised Learning › BigBiGAN

BigBiGAN

4 papers tagged archive 2025-07-28

Introduced by Jeff Donahue et al. in Large Scale Adversarial Representation Learning

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

BigBiGAN is a type of BiGAN with a BigGAN image generator. The authors initially used ResNet as a baseline for the encoder ℰ followed by a 4-layer MLP with skip connections, but they experimented with RevNets and found they outperformed with increased network width, so opted for this type of encoder for the final architecture.

Paper Source See Code · LEGO999/BigBiGAN-TensorFlow2.0

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Diffusion Models Beat GANs on Image Classification
17 Jul 2023 · 1 repository · arXiv:2307.08702
Object Segmentation Without Labels with Large-Scale Generative Models
8 Jun 2020 · 1 repository · arXiv:2006.04988
Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN
31 Jan 2020 · 0 repositories · arXiv:2001.11761
Large Scale Adversarial Representation Learning
4 Jul 2019 · 4 repositories · arXiv:1907.02544

Syntology ran 0 of 3 samples · 3 unverified

Tasks archive 2025-07-28

20 shown of 23 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

Task	Papers
Image Classification	2
Image Generation	2
image-classification	2
Attribute	1
Classification	1
Contrastive Learning	1
Denoising	1
Fine-Grained Image Classification	1
Generative Adversarial Network	1
Image Reconstruction	1
Object	1
Representation Learning	1
Saliency Detection	1
Segmentation	1
Self-Supervised Image Classification	1
Self-Supervised Learning	1
Semantic Segmentation	1
Semi-Supervised Image Classification	1
Super-Resolution	1
Transfer Learning	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Self-Supervised Learning Generative Adversarial Networks Generative Models

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections